Customer-obsessed science

The 21st century workforce is increasingly characterized by more flexible labor models, particularly in e-commerce and supply chain operations. While previous research has focused mostly on last-mile, on-the-road settings, we focus on under-the-roof (UTR) environments, which present unique challenges due to their complex, varied tasks requiring training and experience. Our study addresses the need to better

Multi-model inference systems—whether based on routing, cascading, or unified strategies—often rely on confidence signals to decide when a small language model (SLM) output should be accepted or deferred. While such signals are commonly used in classification and short-form generation, their reliability in structured generation settings remains poorly understood. In this work, we study log-probability confidence

Large Language Models (LLMs) perform well on short-horizon tasks but struggle with long-horizon, multimodal scenarios that require multi-step reasoning, perception, and adaptive planning. We identify two key challenges in these settings: the difficulty of long-term coordination between planning and execution within single-agent architectures and the inefficiency of indiscriminate visual grounding. To address

We present a protocol for fault-tolerantly implementing the logical quantum random access memory (QRAM) operation, given access to a specialized, noisy QRAM device. For coherently accessing classical memories of size 2^n, our protocol consumes only poly(n) fault-tolerant quantum resources (logical gates, logical qubits, quantum error correction cycles, etc.), avoiding the need to perform active error correction

This paper investigates synthetic data generation strategies in developing generative retrieval models for domain-specific corpora, thereby addressing the scalability challenges inherent in manually annotating in-domain queries. We study the data strategies for a two-stage training framework: in the first stage, which focuses on learning to decode document identifiers from queries, we investigate LLM-generated